Post on 07-Feb-2018
Analysing Wi-Fi/LTE Coexistence to Demonstratethe Value of Risk-Informed Interference Assessment
Andra M. Voicu, Ljiljana SimicInstitute for Networked Systems
RWTH Aachen University
Email: {avo, lsi}@inets.rwth-aachen.de
J. Pierre de VriesSilicon Flatirons Center
University of Colorado, Boulder
Email: pierredv@hotmail.com
Marina Petrova, Petri MahonenInstitute for Networked Systems
RWTH Aachen University
Email: {mpe, pma}@inets.rwth-aachen.de
Abstract—Effective interference evaluation methods are crucialwhen making regulatory decisions about whether new wirelesstechnologies should be allowed to operate. Such decisions arehighly relevant for both DSA technologies in licensed bands andfor technologies coexisting in unlicensed bands. In this paper wedemonstrate the benefit of risk-informed interference assessmentas an effective method that aids spectrum regulators in makingdecisions, and readily conveys engineering insight. We applyrisk assessment to a Wi-Fi/LTE coexistence study in the 5 GHzunlicensed band. Our contributions are: (i) we apply, for the firsttime, risk assessment to a real-life problem of inter-technologyspectrum sharing; and (ii) we demonstrate that risk assessmentcomprehensively quantifies the effect of interference in an in-tuitive manner. We perform extensive Monte Carlo simulationsand we consider throughput degradation and fairness metricsto assess the risk of co- and adjacent channel interference fordifferent network densities, numbers of available channels, anddeployment scenarios. Our risk assessment results show that noregulatory intervention is needed to ensure harmonious technicalcoexistence between Wi-Fi/LTE in the unlicensed band. As anengineering insight, Wi-Fi coexists better with itself in locallydense deployments, but better with LTE in sparse deployments.For the large number of available channels typically expectedin practice in the 5 GHz band, the risk of interference forWi-Fi coexisting with LTE is negligible, rendering policy andengineering concerns largely moot.
Index Terms—coexistence, interference, LTE, risk assessment,spectrum regulation, Wi-Fi.
I. INTRODUCTION
Inter-technology spectrum sharing may generate coexistence
problems in bands where mutual interference among different
systems occurs. Dynamic spectrum access (DSA) techniques
seek to solve such problems by allowing access to the spec-
trum on a primary-secondary basis, where the primary has
priority over secondary systems [1]. This problem is managed
by each technology individually in the unlicensed bands,
where all systems have equal rights to access the spectrum.
Regardless of the spectrum access rights, inter-technology
spectrum sharing raises a two-stage question: (i) which tech-
nologies should/can coexist based on the expected harm of mu-
tual interference, and (ii) how to manage interactions between
technologies on a moment-by-moment basis? In this paper we
present an extended case study of applying risk assessment
for Wi-Fi/LTE coexistence in the unlicensed bands, in order
to evaluate the harm caused by interference.
Evaluating coexistence problems due to co- and adjacent
channel interference is of interest to both spectrum regulators
seeking to establish operational bounds and to engineers
designing and managing systems for optimized performance
within the regulatory restrictions. Assessing interference is
not a trivial task; consequently, most of the studies manage
this complexity by considering worst-case scenarios as the
baseline. Nevertheless, it is not clear how often or under
what conditions such worst-case scenarios would occur in
practice. Making regulatory decisions based on worst-case
analysis may even lead to a complete exclusion of new
entrant technologies, so that the second question of interfer-
ence management becomes irrelevant. As such, comprehensive
interference assessment methods are essential for creating a
regulatory environment that would enable the deployment of
advanced spectrum-sharing techniques, e.g. for DSA-like sce-
narios. Effective interference assessment methods are equally
important for the engineers who design, deploy, and manage
networks of different technologies coexisting in shared bands,
e.g. IEEE 802.11g and n, and Wi-Fi/LTE in the unlicensed
bands. Coexistence performance optimization of such net-
works cannot be conducted under worst-case conditions only.
In this paper we demonstrate the benefit of risk assess-
ment as a complement to worst-case interference analysis.
Importantly, risk assessment is a very new method in the
fields of communications engineering and spectrum regula-
tion, although it has been used successfully in other fields
(e.g. the nuclear industry [2]). We apply risk-assessment to
a Wi-Fi/LTE coexistence study in the 5 GHz unlicensed
band for different network densities, number of channels, and
scenarios. Our contributions are: (i) we are the first to apply
risk-informed interference assessment to a real-life, topical
problem (dealing with inter-technology spectrum sharing with
wide relevance for regulatory DSA-like scenarios); and (ii)
we demonstrate the benefit of risk assessment as a method
that comprehensively and quantitatively characterizes the harm
caused by interference in an intuitive and illustrative manner,
from both policy and engineering perspectives. Our analysis
shows that no regulatory intervention is needed to ensure
harmonious technical coexistence1 between Wi-Fi/LTE in the
1Considering economic and policy coexistence issues, e.g. deploying LTE-in-unlicensed for anti-competitive practices, is out of the scope of this paper.
unlicensed bands. From an engineering perspective, we show
that Wi-Fi coexists better with itself and worse with LTE in
locally dense deployments, but the opposite holds in sparse
deployments, due to the specifics of Wi-Fi’s MAC. Also, given
the large number of available channels expected in practice
in the 5 GHz band, there is typically no risk of interference
caused by LTE-in-unlicensed entrants, which renders both
policy and engineering coexistence issues largely irrelevant.
This paper is organised as follows. Section II gives a brief
overview of LTE-in-unlicensed and prior work on its coexis-
tence with Wi-Fi. Section III presents the risk-informed inter-
ference assessment method. Section IV presents our simulation
and throughput model. Section V illustrates and discusses
the benefit of applying the risk assessment method for our
Wi-Fi/LTE case study and Section VI concludes the paper.
II. LTE-IN-UNLICENSED: THE STORY SO FAR
LTE operation in the unlicensed 5 GHz band has recently
been proposed by industry [3], [4]. Initially, the unlicensed
band is aggregated only for user data transmissions, while the
control traffic is sent over the licensed bands for reliability
reasons [5]. Two main LTE-in-unlicensed variants with funda-
mentally different MAC mechanisms have emerged: (i) LTE-U
proposed by the LTE-U Forum [3]; and (ii) Licensed Assisted
Access (LAA) first standardized by 3GPP in Release 13 [5].
LTE-U is based on an adaptive duty cycle MAC mechanism,
which adjusts the periodic transmission duration of the devices
according to the number of other devices operating in the
same channel, such that all devices have an equal share of
the channel in time. However, LTE-U devices do not sense
and defer to ongoing transmissions before starting their own
transmissions, so collisions are likely. LTE-U is a pre-standard
version intended for markets where listen-before-talk (LBT) is
not required by regulators (e.g. the U.S.).
LAA is based on LBT, a MAC mechanism in which devices
start transmitting only after detecting that the channel is
unoccupied. LBT is required by spectrum regulators in some
regions (e.g. Europe), so LAA was proposed as a globally
applicable standard.
As Wi-Fi is currently the dominant technology in the 5 GHz
band, it has been claimed by some parties (e.g. [6]) that in-
troducing LTE-in-unlicensed would harm Wi-Fi operation. On
the other hand, proponents have argued that LTE-in-unlicensed
would actually improve Wi-Fi performance compared to Wi-Fi
coexisting with itself [3], [4]. The debate between the two
camps led the FCC to issue a Public Notice requesting
comments on LTE coexistence in the unlicensed bands [7],
implicitly raising the question of whether regulatory interven-
tion is required to ensure harmonious technical coexistence
between LTE-in-unlicensed and Wi-Fi.
Most existing Wi-Fi/LTE coexistence analyses are not thor-
ough enough to answer the public policy question of whether
LTE is a friend or a foe to Wi-Fi in the unlicensed band.
Some existing work lacks a detailed description of algo-
rithms and models (e.g. [3]), so that it is difficult to draw
generalizable conclusions from the presented results. Other
work considers only one main LTE-in-unlicensed variant (cf.classification of related work in [8]), so that the results only
partially characterize the Wi-Fi/LTE coexistence problem. In
our previous work [9] we presented results from a transparent,
systematic, and extensive coexistence study and we showed
that LTE-in-unlicensed is neither friend nor foe to Wi-Fi.
In this paper we extend our previous work by conducting,
for the first time, a risk assessment of the Wi-Fi/LTE coex-
istence problem, in order to show the effectiveness of this
method for deriving regulatory and engineering insight from
quantitative results in a comprehensive, illustrative, and intu-
itive manner. Furthermore, we extend our throughput model
from [8] by incorporating adjacent channel interference and
we consider throughput fairness as an additional coexistence
performance metric. Finally, we present more detailed results
than in [8], [9] by showing the full distributions of our
considered metrics.
III. RISK-INFORMED INTERFERENCE ASSESSMENT
A. Introduction to Risk Assessment
Risk-informed interference assessment was introduced as
a comprehensive, quantitative tool for a spectrum regulator
seeking to balance the interests of incumbents, new entrants
and the public when deciding whether and how to allocate new
radio services [10]. It facilitates a balanced assessment of the
adverse technical impact of new entrants on incumbents.
Engineering risk assessment, a well-established method
used in many industries, considers the likelihood-consequence
combinations for multiple hazard scenarios, and complements
a “worst case” analysis that considers the single scenario
with the most severe consequence, regardless of its likelihood.
Charts that plot the severity of hazards against their likelihoods
are frequently used to visualize and compare the risk of
different hazards; see Fig. 2(b).
To date, quantitative risk assessment has not been used
in spectrum management. The authors in [11] proposed a
four-step method for performing risk-informed interference
assessment: (1) make an inventory of all significant harmful
interference hazard modes; (2) define a consequence metric to
characterize the severity of hazards; (3) assess the likelihood
and consequence of each hazard mode; and (4) aggregate
them into a basis for decision making. In [10] it was shown
how this method could be used to analyse the risk of cellular
interference to weather satellite earth stations for a hypothet-
ical general case. By contrast, we are the first to apply risk-
informed interference assessment to a real-life problem and to
inter-technology coexistence in the same spectrum band.
B. Applying Risk Assessment to Wi-Fi/LTE Coexistence
In this paper we address co- and adjacent channel in-
terference from Wi-Fi/LAA/LTE-U entrants towards Wi-Fi
incumbents by applying risk assessment. In Section IV we
present the interference hazards corresponding to Step (1). In
Section III-C we define the throughput consequence metrics
to characterize hazard severity for Step (2). In Section V we
demonstrate Steps (3) and (4) by assessing the hazard modes
TABLE ISCENARIOS AND ENTRANT VARIANTS
PARAMETER
SCENARIO Indoor/indoor(indoor incumbent,
indoor entrant)
Outdoor/outdoor(outdoor incumbent,
outdoor entrant)
Network size incumbent: 10 APsentrant: 1–30 APs
incumbent: 10 APsentrant: 1–10 APs
Maximum number ofavailable channels (Europe) 19 11
Coexistencemechanism
Channelselection
incumbent: random or single channelentrant: random or sense (select channel with fewest incumbent APs) or single channel
MAC
incumbent: Wi-Fi: LBT, CS threshold of -82 dBm for co-channel Wi-Fi devices,and -62 dBm for co-channel non-Wi-Fi and all adjacent channel devices
entrant:LAA: LBT, CS threshold of -62 dBm
LTE-U: ON/OFF with adaptive duty cycle based on number of entrant& incumbent APs within CS range (CS threshold = -62 dBm)
Wi-Fi: LBT, CS threshold of -82 dBm for co-channel Wi-Fi devices,and -62 dBm for co-channel non-Wi-Fi and all adjacent channel devices
PHY
incumbent: Wi-Fi: IEEE 802.11n spectral efficiency ρWiFi, noise figure NF=15 dBentrant:
LAA: LTE spectral efficiency ρLTE , NF=9 dBLTE-U: LTE spectral efficiency ρLTE , NF=9 dBWi-Fi: IEEE 802.11n spectral efficiency ρWiFi, NF=15 dB
LBT parameters& assumptions
binary exponential random backoff with CWmin=15, CWmax=1023,time slot duration σ=9 μs, SIFS=16 μs, DIFS=SIFS+2σ=34 μs (cf. IEEE 802.11)
LBT frameduration Tf
Wi-Fi: Tf = fn(rate, MSDU, PHYheader, MACheader),MSDU=1500 Bytes, PHYheader=40 μs, MACheader=320 bits (cf. IEEE 802.11)
LAA: Tf=1 ms (i.e. duration of LTE subframe)Duty cycle ON-time LTE-U: 100 ms (i.e. maximum ON-time specified in [4])
User distribution 1 user per APTraffic model downlink full-buffered
Channel bandwidth 20 MHzFrequency band 5 GHz (5150–5350 and 5470–5725 MHz)
AP transmit power 23 dBm
and by showing the effectiveness of risk assessment when
making decisions of regulatory and engineering concern.
C. Consequence Metrics for Risk Assessment
In this section we define the consequence metrics to char-
acterize the severity of the interference hazards. In the context
of Wi-Fi/LTE coexistence we select two throughput metrics2
that represent the hazard consequence for the incumbents:
(i) the throughput degradation, which we consider the most
relevant metric to quantify whether Wi-Fi gets a fair share of
the channel and whether it experiences excessive interference
when coexisting with LTE-in-unlicensed, and thus to answer
the technical public policy coexistence question; and (ii) the
throughput unfairness among incumbents, which gives insight
into engineering optimization of inter-technology coexistence
within the given regulatory context.
We define the throughput degradation of the incumbent
access points (APs) when coexisting with entrant APs with
respect to two different baselines: (i) the standalone Wi-Fi
incumbent network, in order to capture the general throughput
degradation due to network densification; and (ii) the Wi-Fi
2We note that throughput has been the baseline network performanceevaluation metric in general and is also considered the only or primaryperformance metric in important Wi-Fi/LTE coexistence studies, e.g. [3].Although delay can be considered a relevant evaluation metric in some cases,it is typically applied for VoIP traffic [12], which does not represent themajority of the traffic.
incumbent network coexisting with a Wi-Fi entrant network,
in order to directly focus on the question of whether LTE is
a better neighbour to Wi-Fi than Wi-Fi is to itself. For an
incumbent AP x we estimate the throughput degradation as
ΔRx =Rx,baseline −Rx
Rx,baseline, (1)
where Rx,baseline is the baseline throughput of x and Rx is the
throughput of x when coexisting with a given entrant variant.In order to quantify the throughput fairness among incum-
bent APs, we apply Jain’s fairness index [13] for a set of
incumbent throughput results corresponding to APs in a single
network realization, given by
J =|∑n
x=1 Rx|2n∑n
x=1 R2x
, (2)
where n is the number of incumbents in the network.For consistency with data representation in a risk assessment
chart (explained in Section V-A), we define the incumbent
throughput unfairness as the consequence metric, given by
U = 1− J. (3)
IV. SIMULATION & THROUGHPUT MODELS
A. Simulation ModelWe assume a population of Wi-Fi incumbent APs coexisting
with Wi-Fi/LAA/LTE-U entrant APs in two main scenarios,
for realistic network densities, as summarized in Table I.
We assume the incumbent APs and their associated users are
always Wi-Fi devices implementing the IEEE 802.11n PHY
layer and LBT3 at the MAC layer with a carrier sense (CS)
threshold of -82 dBm for deferring to co-channel Wi-Fi
devices, and -62 dBm for adjacent channel Wi-Fi devices and
co- and adjacent channel non-Wi-Fi devices. For the entrants,
we assume either (i) LAA implementing the LTE PHY and
the LBT MAC mechanism with -62 dBm CS threshold for
deferring to all other devices, or (ii) LTE-U that adapts its duty
cycle according to the number of detected APs based on the
-62 dBm CS threshold. As the baseline entrant for answering
the question of whether LTE-in-unlicensed is a friend or a foe
to Wi-Fi, we also consider (iii) Wi-Fi entrants.We consider two main scenarios, where each AP has
one associated user, i.e. the indoor/indoor scenario where
all incumbent and entrant devices are located indoors and
the outdoor/outdoor scenario where all devices are located
outdoors, as in Fig. 1. For the indoor/indoor scenario we
assume a single-floor building, according to the 3GPP dual
stripe model [14]. Each incumbent AP and its associated user
are located randomly within a single apartment. The entrant
APs and their associated users are first randomly located in
unoccupied apartments and then randomly occupy apartments
with only one other AP, until all apartments contain up to two
APs. This results in network densities of 600–12000 APs/km2,
as (and more) dense as that seen in contemporary 2.4 GHz de-
ployments, but not yet in 5 GHz [15]. For the outdoor/outdoorscenario we assume 20 real outdoor base station locations from
central London [16] and we randomly overlay buildings over
the area where the real outdoor locations were observed, re-
sulting in network densities of 7–150 APs/km2. The associated
users are located within the coverage area of the respective
APs and at a maximum distance of 50 m. As a worst-case
interference scenario of low signal attenuation through walls
resulting in high interference among APs, we also consider
the indoor/indoor scenario without internal walls.We assume each incumbent AP randomly selects one of
the available channels. The entrants either randomly select
a channel, i.e. random, or apply sense, i.e. they randomly
select a channel unoccupied by incumbents. We assume the
maximum number of channels in the 5 GHz band in Europe to
be typically available in practice (i.e. 19 indoor and 11 outdoor
channels), or only the 4 non-DFS channels, corresponding
to less likely cases of either legacy devices that do not
implement DFS, or devices with faulty DFS implementation
(e.g. erroneously detecting radar channels as occupied). As a
worst-case of high local AP density corresponding to a high
level of interference, we also consider the single channel case.We assume propagation models according to the indoor and
outdoor links in our scenarios. For the indoor links we assume
a multi-wall-and-floor model (MWF) model [19] and for the
outdoor links we assume the ITU-R model for line-of-sight
(LOS) propagation within street canyons and for non-line-of-
3We note that CSMA/CA is a specific variant of the more general LBTmechanism, so we refer to it as LBT. In this paper we assume LBT withbinary exponential random backoff throughout.
(a) Indoor/indoor scenario: the incumbents and entrants are lo-cated inside a single-floor building with 20 apartments (each of10 m × 10 m × 3 m). Each AP and its associated user are randomlyplaced in a single apartment with up to two AP-user pairs. This figureshows an example of the most dense deployment.
(b) Outdoor/outdoor scenario: the incumbent and entrant APs arerandomly allocated one real outdoor location and are placed at theroof-top level. The outdoor users are located in the coverage areaof and at a maximum distance of 50 m from the AP that they areassociated with, at a height of 1.5 m. The length of the buildingsis randomly selected between 3–10 apartments and the height israndomly selected between 3–5 floors. The size of the total studyarea is 346 m × 389 m, corresponding to the area in London wherethe real locations of the outdoor APs were observed.
Fig. 1. Example network layout based on the 3GPP dual stripe modelfor indoor deployments and real outdoor picocell locations for outdoordeployments, for the (a) indoor/indoor, and (b) outdoor/outdoor scenarios,showing locations of incumbent APs (�), incumbent users (�), entrant APs(�), and entrant users (•).
sight (NLOS) with over roof-top propagation [20]. We assume
log-normal shadowing with a standard deviation of 4 dB for
indoor links and 7 dB for outdoor links [21].
We perform extensive Monte Carlo simulations in MATLAB
with 3000 network realizations for the indoor/indoor scenario
and indoor/indoor scenario without internal walls, and 1500
realizations for the outdoor/outdoor scenario. We assume
downlink saturated traffic (i.e. most challenging coexistence
case) and we evaluate the network performance based on the
downlink throughput per AP, estimated at the associated user.4
B. Throughput Model
Our throughput and interference model for co-channel in-
terference is described in detail in [8] and in this paper we
4For multiple users associated to a single AP, the user throughput is obtainedby dividing the per-AP throughput to the number of associated users.
TABLE IIPARAMETERS FOR THROUGHPUT AND INTERFERENCE MODEL
Parameter
AP typeIncumbent Entrant
Sx defined in [8]defined in [8], if W-Fi/LAA entrant
1, if LTE-U entrant
rdeg,x0, if Wi-Fi/LAA entrant
defined in [8], if LTE-U entrant0
AirT imex
11+|Ax|+|Bx| , if Wi-Fi/LAA entrant
∏y∈Bx
(1− 1
1 + |Cy |+ |Dy |)× 1
1 + |Ax|,
if LTE-U entrant
11+|Ax|+|Bx|
ρx ρWiFi [17]ρWiFi, if Wi-Fi entrant
ρLTE [18], if LAA/LTE-U entrant
Icou∑
z∈(Aco\Acox )∪(Bco\Bco
x )
Pz ×AirT imez
Lu,z
∑z∈(Aco\Aco
x )∪(Bco\Bcox )
Pz ×AirT imez
Lu,z, if Wi-Fi/LAA entrant
∑z∈(Aco\Aco
x )∪(Bco)
Pz ×AirT imez
Lu,z, if LTE-U entrant
Iadju
∑
z∈(Aadj\Aadjx )∪(Badj\Badj
x )
Pz ×AirT imez
Lu,z ×ACIRu,z
∑
z∈(Aadj\Aadjx )∪(Badj\Badj
x )
Pz ×AirT imez
Lu,z ×ACIRu,z, if Wi-Fi/LAA entrant
∑
z∈(Aadj\Aadjx )∪(Badj)
Pz ×AirT imez
Lu,z ×ACIRu,z, if LTE-U entrant
apply it to both co- and adjacent channel interference.
For Wi-Fi and LAA, we assume the LBT mechanism does
not allow co- and adjacent channel APs within CS range5
of each other to transmit simultaneously. Each of these APs
is thus allowed to transmit for only an approximately equal
fraction of time. The co- and adjacent channel APs located
outside the CS range interfere by decreasing the signal-to-
interference-and-noise-ratio (SINR) at the associated user.
For LTE-U, the adaptive duty cycle MAC mechanism ad-
justs the duty cycle of each AP based on the number of co- and
adjacent channel APs detected within the CS range. However,
the LTE-U APs within the same CS range may interfere with
each other, as they do not check if the channel is unoccupied
before transmitting. Instead, they transmit periodically, where
we assume uncoordinated LTE-U APs that randomly select
the starting moment of their duty cycle period, so that their
transmissions may overlap in time. The Wi-Fi incumbents
sense the medium unoccupied by coexisting LTE-U entrants
for a duration determined by the entrants’ adaptive duty cycle,
and the likelihood of their overlapping transmissions. Conse-
quently, when coexisting with LTE-U entrants, the incumbents
detect the medium unoccupied for a different fraction of time
than when coexisting with LAA entrants. The co- and adjacent
channel LTE-U APs located outside the CS range decrease the
SINR at the associated incumbent or entrant user.
In general we estimate the downlink throughput of an AP xaccording to our model in [8] as
Rx = Sx × (1− rdeg,x)×AirT imex × ρx(SINRu),(4)
5The CS range within which co- and adjacent channel APs are located isdefined according to the respective CS thresholds given in Section IV-A.
where Sx is the LBT MAC protocol efficiency accounting
for sensing time and collisions between LBT frames based
on Bianchi’s model [22], rdeg,x is the additional throughput
degradation due to collisions between LBT and duty cycle
frames, AirT imex is the fraction of time that AP x is allowed
to transmit according to its own and the other within-CS-range
APs’ MAC mechanisms, ρx is the auto-rate function mapping
the SINR to the bit rate, and SINRu is the SINR at the
associated user u of x. A mathematical description of these
parameters in given in Table II, where Ax is the set of co-
and adjacent channel incumbent APs within CS range of x,
Bx is the set of co- and adjacent channel entrant APs within
CS range of x, |Ax| is the number of co- and adjacent channel
incumbent APs within the CS range of x, |Bx| is the number
of co- and adjacent channel entrant APs within the CS range of
x, |Cy| is the number of co- and adjacent channel incumbent
APs within CS range of AP y, and |Dy| is the number of co-
and adjacent channel entrant APs within CS range of AP y.
We estimate the SINR at the associated user u as
SINRu =Px(Lu,x)
−1
Icou + Iadju +N0
, (5)
where Px is the transmit power of AP x, Lu,x is the propaga-
tion loss between user u and AP x, Icou is the interference from
co-channel APs, Iadju is the interference from adjacent channel
APs, and N0 is the background noise (assumed -174 dBm/Hz).
A mathematical description of these terms is given in Table II,
where Aco is the set of all co-channel incumbent APs of x,
Acox is the set of co-channel incumbent APs within CS range
of x, Bco is the set of all co-channel entrant APs of x, Bcox is
the set of co-channel entrant APs within CS range of x, Aadj
is the set of all adjacent channel incumbent APs of x, Aadjx
is the set of adjacent channel incumbent APs within CS range
of x, Badj is the set of all adjacent channel entrant APs of
x, Badjx is the set of adjacent channel entrant APs within CS
range of x, Pz is the transmit power of AP z, AirT imez is
the fraction of time AP z may transmit (defined similarly as
AirT imex), Lu,z is the propagation loss between z and u,
and ACIRu,z is the adjacent channel interference ratio given
by z’s transmitter at u’s receiver when operating on adjacent
channels. We assume the model in [5] defining ACIRu,z as
ACIRu,z =1
1ACLRz
+ 1ACSu
, (6)
where ACLRz is the adjacent channel leakage ratio of
transmitter z and ACSu is the adjacent channel selec-
tivity of receiver u. For Wi-Fi APs and users we as-
sume ACLRz=26 dBm and ACSx=ACSu=22 dBm, cor-
responding to the least efficient Wi-Fi transmitter and re-
ceiver,6 whereas for the LTE-in-unlicensed variants we assume
ACLRz=45 dBm, ACSx=46 dBm, and ACSu=22 dBm [5],
corresponding to the most efficient LTE AP transmitter and
receiver, and the same LTE user receiver as for Wi-Fi.
V. RESULTS & ANALYSIS
In this section we present a selection of our simulation
results that illustrate the effectiveness of risk assessment for
Wi-Fi/LTE coexistence. Specifically, we evaluate the risk of
co- and adjacent channel interference for the Wi-Fi incum-
bents7 and we show its relevance for spectrum regulators, i.e.
in deciding whether regulatory action is required to ensure
harmonious inter-technology coexistence, and for engineers
designing and optimizing such networks.
We apply the consequence metrics defined in Section III-C
as follows. The throughput degradation is estimated for each
incumbent AP in each Monte Carlo network realization, re-
sulting in a distribution of throughput degradation over all
incumbents in all network realizations. Jain’s unfairness is es-
timated for each network realization, over the set of incumbent
throughput values within a single network realization, resulting
in a distribution of unfairness over all network realizations.
In this section we first discuss how to read risk assessment
charts in general and for our case study. Then we focus on indi-
vidually assessing the risk of interference for different network
densities, channel availability, and deployment scenarios.
A. Reading Risk Assessment Charts
Risk assessment representations in general are likelihood-
consequence charts where the curves show an increasing risk
of harm from the lower left corner to the upper right corner,
as indicated by the red arrow in the example of Fig. 2(b). For
the Wi-Fi/LTE coexistence case, the likelihood-consequence
6We note ACSx is needed as the power received from adjacent channelsis also estimated at AP x, since its level may be high enough, such that xmay detect the channel busy and may share its channel in time with (or adaptits duty cycle according to) transmissions in the adjacent channels.
7In this paper we seek to illustrate the benefit of risk-informed interferenceassessment by means of example and we focus on the incumbent performance.We thus omit entrant throughput results as they are not presently relevant, butentrant results are reported in our previous work [8], [9].
charts illustrate the risk of interference that the incumbents
suffer when coexisting with different entrants, by following the
general rule of increased risk towards the upper right corner.
We represent the likelihood as the CCDF of the throughput
consequence metrics, for consistency with this rule.
In our figures showing throughput degradation, e.g.
Figs. 2(b) and 2(c), a positive throughput degradation shows
an actual decrease in throughput compared with the considered
baseline, whereas a negative throughput degradation shows an
increase in throughput.
B. Effect of Network Density
In this section we demonstrate the advantage of risk over
conventional representation of our coexistence study results
when assessing interference for various network densities.
Fig. 2 shows an example of conventional and risk represen-
tations of incumbent throughput performance results, for the
indoor/indoor scenario with 10 incumbents and 0–30 entrants,
for single channel (i.e. co-channel interference only). Specifi-
cally, Fig. 2(a) shows an example of a conventional represen-
tation as the CDF of the incumbent AP throughput Rx. When
the number of entrants increases from 0 to 30, the incumbent
throughput decreases from e.g. 10 to 2.5 Mbps for the median
value. Also, Fig. 2(a) shows that for a fixed number of entrants,
the throughput of incumbents coexisting with Wi-Fi entrants is
sometimes higher and sometimes lower than when coexisting
with LAA/LTE-U entrants. This suggests that LAA/LTE-U
entrants are sometimes friend and sometimes foe to Wi-Fi,
but does not readily provide further insightful information.
Although such a representation of the absolute throughput is
important for coexistence cases since it provides the baseline
for calculating the throughput degradation as a relative metric,
the performance degradation caused by various entrants cannot
be quantified in a straightforward way.
Fig. 2(b) shows the results in Fig. 2(a) in the form of
a likelihood-consequence chart, i.e. the CCDF vs. incum-
bent throughput degradation with the standalone incumbent
throughput (i.e. no entrant) as baseline. Fig. 2(b) shows in
general that the risk increases significantly when the number
of entrants increases, irrespective of the entrant technology.
The median incumbent throughput degradation increases from
0% to 75% for 0 to 30 entrants. Also, for each number of
entrant APs there is a switching point where the order of the
curves corresponding to Wi-Fi and LAA/LTE-U is reversed.
Consequently, the risk of incumbent throughput degradation
when coexisting with Wi-Fi entrants is sometimes higher and
sometimes lower than when coexisting with LAA/LTE-U.
LTE-in-unlicensed is thus neither consistently friend nor foe
to Wi-Fi, suggesting the engineering policy question is moot.
From a more detailed engineering perspective, it is evident
from Fig. 2(b) that the Wi-Fi entrants pose greater risk
in case of lower negative impact, whereas the LAA/LTE-U
entrants pose greater risk in case of higher negative impact.
Let us consider the example case of 30 entrants, where the
switching point occurs at a throughput degradation of 72%.
For a throughput degradation lower than 72%, the risk posed
0 5 10 15 20 25 30 35 40 45 50 55 60 650
0.1
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1C
DF
Throughput per incumbent AP (Mbps)
NO entrant1 entrant: Wi−Fi5 entrants: Wi−Fi10 entrants: Wi−Fi30 entrants: Wi−Fi1 entrant: LAA5 entrants: LAA10 entrants: LAA30 entrants: LAA1 entrant: LTE−U adaptive duty cycle5 entrants: LTE−U adaptive duty cycle10 entrants: LTE−U adaptive duty cycle30 entrants: LTE−U adaptive duty cycle
(a) Conventional representation
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DF
Throughput degradation per incumbent AP (%)
friend
foe
(b) Risk representation
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DF
Throughput degradation per incumbent AP (%)
(c) Risk representation
Fig. 2. Example of conventional (a) and risk (b) and (c) representationsof incumbent AP performance results for the indoor/indoor scenario, forsingle channel, for 10 incumbent and 0–30 entrant APs, as (a) distribution ofthroughput per incumbent AP; (b) distribution of throughput degradation perincumbent AP with the standalone incumbents as baseline; and (c) distributionof throughput degradation per incumbent AP with the incumbents coexistingwith Wi-Fi entrants as baseline.
by Wi-Fi entrants is higher than for LAA/LTE-U entrants,
whereas for a throughput degradation higher than 72% the
opposite holds. This effect occurs due to the value of the
CS threshold according to which the incumbent APs defer
to the entrants, i.e. the incumbents apply a -82 dBm and
-62 dBm threshold to defer to Wi-Fi and LAA/LTE-U entrants,
respectively (cf. IEEE 802.11). For the lower CS threshold
the incumbents are more conservative and avoid strong in-
terference, by deferring to more entrants and transmitting less
often. A lower CS threshold is thus suitable for (locally) dense
deployments with strong interference, whereas it causes the
incumbents to defer unnecessarily in sparse deployments with
low interference. The opposite holds for a higher CS threshold.
Fig. 2(b) also shows that for a fixed number of entrants, the
throughput degradation from LTE-U and LAA is similar, so
LTE-U and LAA are almost equally good neighbours to Wi-Fi.
The risk is somewhat higher from LTE-U than LAA, due to the
additional collisions in term rx,deg and the adjustment of the
entrant duty cycle based on the number of devices detected by
the entrants only. Consequently, some incumbents are allowed
to transmit for a lower fraction of time than their equal share
when considering the number of APs in their own CS range.8
Finally, Fig. 2(b) shows that some of the incumbents have a
negative throughput degradation when coexisting with entrants
compared with the standalone (i.e. no entrant) network. These
cases are due to hidden nodes that are continuous sources
of interference in the standalone incumbent network, but that
interfere only for a fraction of time when they defer to entrants
deployed in the coexistence cases. Also, the negative through-
put degradation is in some cases an artefact of our throughput
model, where the MAC efficiency term Sx is averaged over
the entire CS range, sometimes resulting in higher average
values for the incumbents when LTE-in-unlicensed entrants
with higher Sx are located within the CS range.
In order to focus directly on the question of whether LTE-in-
unlicensed is friend or foe to Wi-Fi, Fig. 2(c) shows an alter-
native risk representation of Fig. 2(b), where the baseline for
incumbent throughput degradation is the incumbent throughput
when coexisting with Wi-Fi entrants. A positive throughput
degradation thus corresponds to LTE being foe, whereas a
negative throughput degradation corresponds to LTE being
friend to Wi-Fi in unlicensed bands. For a given number of
entrants, the percentage of incumbents for which the entrants
are friends or foes is similar, with up to 50% being friends
and 50% foes for 30 entrants. This clearly shows that for the
typical indoor/indoor scenario no regulatory intervention is
required. In the rest of this paper we focus on the throughput
degradation with only the standalone incumbent network as
baseline (as in Fig. 2(b)), as this case provides better insight
into the more general network densification problem.
Let us now consider the second consequence metric, i.e.
Jain’s unfairness. Fig. 3 shows an example of conventional and
risk representations of Jain’s fairness/unfairness among incum-
bents, for the indoor/indoor scenario with 10 incumbents and
0–30 entrants, corresponding to the throughput degradation in
Fig. 2. Specifically, Fig. 3(a) shows a conventional represen-
8The opposite effect was shown in [8] for low incumbent and high entrantdensities, where the likelihood of short duty cycles and overlapping entranttransmissions is higher, such that the incumbents find the medium unoccupiedby entrants for a longer fraction of time.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1C
DF
Fairness (Jain’s index) for incumbent APs
NO entrant1 entrant: Wi−Fi5 entrants: Wi−Fi10 entrants: Wi−Fi30 entrants: Wi−Fi1 entrant: LAA5 entrants: LAA10 entrants: LAA30 entrants: LAA1 entrant: LTE−U adaptive duty cycle5 entrants: LTE−U adaptive duty cycle10 entrants: LTE−U adaptive duty cycle30 entrants: LTE−U adaptive duty cycle
(a) Conventional representation
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
CC
DF
Unfairness for incumbent APs
(b) Risk representation
Fig. 3. Example of conventional (a) and risk (b) representations of incumbentAP performance results for the indoor/indoor scenario, for single channel, for10 incumbent and 0–30 entrant APs, as (a) distribution of Jain’s fairness indexfor incumbent APs in each network realization; and (b) distribution of Jain’sunfairness index for incumbent APs in each network realization.
tation as the CDF of the fairness index J . For consistency
with the likelihood-consequence charts, Fig. 3(b) shows the
same results as Fig. 3(a) in the form of CCDF of throughput
unfairness U , where the risk increases towards the upper
right corner. We will thus comment only on Fig. 3(b). For
a fixed number of entrants, the risk of incumbent unfairness
is higher for LAA/LTE-U entrants than for Wi-Fi entrants,
consistent with our results in Fig. 2(b), which show that the
risk of high throughput degradation is higher for LAA/LTE-U,
resulting in larger variation of the throughput degradation.
Also, the risk of unfairness increases with the number of
entrants for LAA/LTE-U, whereas it decreases for Wi-Fi,
given the different CS thresholds that the incumbents apply.
Moreover, the risk of unfairness decreases for Wi-Fi below the
risk for the standalone incumbent network. Also, LTE-U has a
higher risk of unfairness compared with LAA, consistent with
its higher throughput degradation for only some incumbents.
Importantly, our results show that for single channel the risk
is qualitatively different for the two considered consequence
metrics. The risk of throughput degradation (relevant for the
engineering policy question) in Fig. 2(b) is sometimes higher
and sometimes lower for coexistence with LAA/LTE-U than
with Wi-Fi (i.e. LAA/LTE-U is sometimes friend and some-
times foe). By contrast, the risk of Jain’s unfairness among
incumbents (relevant for engineering performance optimiza-
tion) in Fig. 3(b) is always higher with LAA/LTE-U than with
Wi-Fi (i.e. with Wi-Fi, all incumbents are affected in a similar
way). This illustrates the importance of choosing a metric
that effectively quantifies policy goals, as different metrics,
encoding different values, may lead to different conclusions.
C. Effect of Channel Availability
In this section we assess the risk of interference for the
Wi-Fi/LTE coexistence case, for different numbers of channels
(i.e. with co- and adjacent channel interference) and channel
selection schemes. Fig. 4 shows the incumbent throughput
degradation and Jain’s unfairness, for the indoor/indoor sce-
nario, for 10 incumbents and 10 entrants (i.e. an example with
a single AP in each apartment), and 1, 4 and 19 channels
with sense and random. The risk of throughput degradation
in Fig. 4(a) increases when the number of channels decreases,
from 0% median throughput degradation for 19 channels to
40-50% median throughput degradation for single channel.For sense with the maximum number of 19 channels
(typically available in practice), near-perfect coexistence is
ensured between incumbents and entrants (i.e. 0% incumbent
throughput degradation), due to the large number of unoccu-
pied channels that the entrants can select from. Also, randomwith 19 channels has similar performance, with only a small
percentage of incumbent APs suffering a rather low throughput
degradation. This shows that no regulatory or engineering
action is needed to ensure harmonious coexistence. As an
engineering insight, Fig. 4 reveals that sense does not bring
significant benefit for such a high number of channels.
For non-DFS devices operating on 4 channels with the
entrants implementing sense, the throughput degradation is
similar to the one for 19 channels, whereas for 4 channels
with random the throughput degradation increases signifi-
cantly, showing that engineers should implement sense for
the rare cases of such a low number of channels. Also, the
switching point delimiting the friend/foe entrants (explained in
Section V-B) is visible for 4 channels random and for singlechannel; for the other cases LAA/LTE-U is an equally good
or better neighbour to Wi-Fi than Wi-Fi is to itself.
Fig. 4(b) shows the CCDF of Jain’s incumbent unfairness
for 1 to 19 channels, where the unfairness increases when the
number of channels decreases, with the exception of Wi-Fi,
for single channel. The highest unfairness is caused by the
LAA/LTE-U entrants for single channel, but for 4 and 19
channels the unfairness is similar to the one caused by Wi-Fi
entrants, consistent with the similar throughput degradation re-
sults for all entrant technologies for these number of channels.
Importantly, for the typical 19 and also for 4 non-DFS avail-
able channels, both consequence metrics consistently show
that there is no coexistence problem relevant for engineering
policy or engineering optimization.
−100−90 −80 −70 −60 −50 −40 −30 −20 −10 0 10 20 30 40 50 60 70 80 90 1000
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1C
CD
F
Throughput degradation per incumbent AP (%)
(a)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
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1
CC
DF
Unfairness for incumbent APs
10 entrants: Wi−Fi; 1 ch10 entrants: LAA; 1 ch10 entrants: LTE−U; 1 ch10 entrants: Wi−Fi; 4 ch; random10 entrants: LAA; 4 ch; random10 entrants: LTE−U; 4 ch; random10 entrants: Wi−Fi; 4 ch; sense10 entrants: LAA; 4 ch; sense10 entrants: LTE−U; 4 ch; sense10 entrants: Wi−Fi; 19 ch; random10 entrants: LAA; 19 ch; random10 entrants: LTE−U; 19 ch; random10 entrants: Wi−Fi; 19 ch; sense10 entrants: LAA; 19 ch; sense10 entrants: LTE−U; 19 ch; sense
(b)
Fig. 4. Risk representation of incumbent AP performance results for theindoor/indoor scenario, for different number of channels, for 10 incumbentand 10 entrant APs, as (a) distribution of throughput degradation per incum-bent AP with the standalone incumbents as baseline; and (b) distribution ofJain’s unfairness index for incumbent APs in each network realization.
D. Effect of Deployment Scenario
This section shows the benefit of risk assessment when
quantifying the harm of interference in different scenarios,
i.e. indoor/indoor, indoor/indoor without internal walls, and
outdoor/outdoor. Fig. 5 shows how different scenarios affect
the incumbent throughput degradation and Jain’s unfairness for
single channel, for 10 incumbents and 10 entrants. Importantly,
Fig. 5(a) shows a consistent switching point between Wi-Fi
and LAA/LTE-U curves across different scenarios at 40-50%
degradation. LTE-in-unlicensed is thus consistently sometimes
friend and sometimes foe to Wi-Fi, regardless of the scenario.
When comparing different scenarios for a given entrant
technology in Fig. 5(a), we observe the following engineer-
ing insights: (i) the lowest risk of low throughput degrada-
tion is achieved for the outdoor/outdoor scenario and the
highest risk of low degradation for the indoor/indoor sce-
nario without internal walls; (ii) the highest risk of high
throughput degradation is achieved for the outdoor/outdoorscenario and the lowest risk of high degradation for the
indoor/indoor scenario without internal walls; and (iii) for
−100−90 −80 −70 −60 −50 −40 −30 −20 −10 0 10 20 30 40 50 60 70 80 90 1000
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0.9
1
CC
DF
Throughput degradation per incumbent AP (%)
10 entrants: Wi−Fi; in/in10 entrants: LAA; in/in10 entrants: LTE−U; in/in10 entrants: Wi−Fi; in/in w/o walls10 entrants: LAA; in/in w/o walls10 entrants: LTE−U; in/in w/o walls10 entrants: Wi−Fi; out/out10 entrants: LAA; out/out10 entrants: LTE−U; out/out
(a)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
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0.7
0.8
0.9
1
CC
DF
Unfairness for incumbent APs
(b)
Fig. 5. Risk representation of incumbent AP performance results for theindoor/indoor, indoor/indoor without internal walls, and outdoor/outdoorscenarios, for single channel, for 10 incumbent and 10 entrant APs, as (a) dis-tribution of throughput degradation per incumbent AP with the standaloneincumbents as baseline; and (b) distribution of Jain’s unfairness index forincumbent APs in each network realization.
the indoor/indoor scenario there is a moderate risk of high
and low throughput degradation. This shows that the variation
of incumbent throughput is highest in the outdoor/outdoorscenario, moderate for the indoor/indoor scenario, and low for
the indoor/indoor scenario without internal walls. This effect
is consistent with the interference conditions in each scenario.
For the indoor/indoor scenario without internal walls where
the interference is high and the APs are located close to each
other, the incumbents detect more entrants and are able to
better avoid strong interference by deferring to them, at the
expense of sharing the channel in time. Specifically, almost
all incumbents suffer a degradation of at least 20%, and for
coexistence with Wi-Fi entrants the incumbent degradation
is constant and equal to 52%, as every incumbent detects
all incumbents and entrants within CS range and the MAC
efficiency also changes accordingly. In the outdoor/outdoorscenario the AP network deployment is more sparse and the
users are located at a wider range of distances from the APs
that they are associated with. Consequently, users close to
their corresponding APs experience low risk of degradation,
but users far from their corresponding APs may face hidden
node problems (i.e. at least 15% of the APs have a throughput
degradation of 100%). The interference in the indoor/indoorscenario where the APs are separated by walls is moderate
compared with the other scenarios.
We note that in our previous work [8], [9] we observed
that in the indoor/outdoor scenario, i.e. where the incumbents
are located indoors and the entrants are located outdoors,
the incumbents and entrants are isolated from each other,
due to the high attenuation through the external walls. The
corresponding risk of interference from the entrants to the
incumbents would therefore be zero, so we do not present
results for this scenario in this paper.
Fig. 5(b) shows Jain’s throughput unfairness among in-
cumbents for different scenarios. Consistent with our results
in Fig. 5(a) and the corresponding discussion, the lowest
unfairness is achieved for the indoor/indoor scenario w/ointernal walls with down to zero unfairness for incumbents
coexisting with Wi-Fi entrants. A moderate risk of unfairness
is shown for the indoor/indoor scenario, whereas for the
outdoor/outdoor scenario the unfairness is large. Also, for
each specific scenario, the unfairness when coexisting with
Wi-Fi entrants is lower than when coexisting with LAA/LTE-U
entrants, consistent with the values of the CS threshold that
the incumbents implement.
VI. CONCLUSIONS
In this paper we presented a case study of Wi-Fi/LTE
coexistence in the 5 GHz band, in order to demonstrate
the value of risk-informed interference assessment in making
regulatory decisions and for providing engineering insight. We
applied risk assessment methods to this coexistence problem
by (i) identifying co- and adjacent channel interference as haz-
ard modes, (ii) defining the throughput degradation and Jain’s
throughput unfairness as consequence metrics, and (iii) as-
sessing the likelihood and consequence for different network
densities, numbers of available channels, and scenarios (i.e.
indoor/indoor, indoor/indoor without internal walls, and out-door/outdoor). We performed extensive Monte Carlo simula-
tions for Wi-Fi incumbents coexisting with Wi-Fi/LAA/LTE-U
entrants and we estimated the downlink throughput by consid-
ering co- and adjacent channel interference.
Our analysis showed that risk assessment is an effective
method for evaluating the harm caused by interference in
a comprehensive and intuitive manner. This method clearly
showed that LTE-in-unlicensed is neither friend nor foe to
Wi-Fi in general, and thus that no regulatory intervention
is needed to ensure harmonious technical coexistence. From
an engineering perspective, our analysis showed that Wi-Fi
incumbents suffer a lower risk of interference when coexisting
with Wi-Fi entrants compared with LTE-in-unlicensed entrants
in locally dense deployments, but the opposite holds for sparse
deployments, due to the Wi-Fi MAC design. Also, for the high
number of available channels expected in practice, there is
a negligible risk of interference for Wi-Fi incumbents from
LTE-in-unlicensed entrants, which renders both policy and
engineering coexistence issues largely irrelevant.
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